Automated Diagnosis of Lung Cancer with the Use of Deep Convolutional Neural Networks on Chest CT

Kim, Joongwon; Lee, Hojun; Yoon, Taeseon

doi:10.1145/3168776.3168798

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…This ensemble method achieved an average accuracy of 85.91%. Based on a convolutional neural network [16], Joongwon Kim et al designed a system that can automatically diagnose the risk of lung cancer on chest CT. In order to realize the automatic classification of cystic fibrosis lung disease (CFLD) lesion degree in computed tomography (CT) [17], Xi Jiang et al proposed a framework based on deep convolutional neural networks and transfer learning.…”

Section: Related Workmentioning

confidence: 99%

Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Zhou

Xiaoli³

et al. 2023

Electronics

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In the context of population aging, to reduce the run on public medical resources, nursing homes need to predict the health risks of the elderly periodically. However, there is no professional medical testing equipment in nursing homes. In the current disease risk prediction research, many datasets are collected by professional medical equipment. In addition, the currently researched models cannot be run directly on mobile terminals. In order to predict the health risks of the elderly without relying on professional medical testing equipment in the application scenarios of nursing homes, we use the datasets collected by non-professional medical testing equipment. Based on transfer learning and lightweight neural networks, we propose a disease risk prediction model, Diplin (disease risk prediction model based on lightweight neural network), applied to nursing homes. This model achieved 98% accuracy, 97% precision, 96% recall, 95% specificity, 97% F1 score, and 1.0 AUC (area under ROC curve) value on the validation set. The experimental results show that in the application scenario of nursing homes, the Diplin model can provide practical support for predicting the health risks of the elderly, and this model can be run directly on the tablet.

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Section: Related Workmentioning

confidence: 99%

Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Zhou

Xiaoli³

et al. 2023

Electronics

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“…Therefore, the training process is incorporated with the recognition step to minimizing the classification problems and increasing the overall performance. Finally, the recurrent neural network computes the output value using a recursive function, which is done using Equation (10).…”

Section: Lung Cancer Recognitionmentioning

confidence: 99%

“…The introduced system effectively works on the small lung cancer data set. Joongwon Kim et al 10 detecting lung cancer from chest CT images using the two‐dimensional convolution neural network. Initially, captured CT images are analyzed, and the affected region is segmented.…”

Section: Introductionmentioning

confidence: 99%

Analyzing CT images for detecting lung cancer by applying the computational intelligence‐based optimization techniques

Pethuraj

Aboobaider

Salahuddin

2022

Computational Intelligence

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Lung cancer is the most critical disease because it affects both men and women. Most of the time, lung cancer leads to death due to less health care and medical attention. In addition, lung cancer is difficult to identify in earlier stages due to the low‐level symptoms and risk factors. To overcome the complexity, effective techniques must predict lung cancer earlier. To attain the problem statement, an lung cancer identification system is developed with the help of a meta‐heuristic algorithm. The CT imageries obtained from the CIA database are analyzed step by step. The gathered image noise is removed by applying the mean filter, and the affected regions are segmented with the help of the Butterfly Optimization Algorithm‐based K‐Means Clustering (BOAKMC) algorithm. Afterward, various statistical features are derived, and the Supervised Jaya Optimized Rough Set related Feature Selection (SJORSFS) process is used to select the lung features. Finally, the lung cancer is identified using Autoencoder based Recurrent Neural Network (ARNN) classification algorithm, successfully recognizing the lung cancer features. Then the system's efficiency is evaluated using a MATLAB setup; here, 3000 are treated as training images and 2043 for testing images. The effective training enhances overall lung cancer prediction accuracy by up to 99.15%.

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Lung Cancer Detection and Classification from Chest CT Scans Using Machine Learning Techniques

Rehman

Kashif

Abunadi

et al. 2021

2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA)

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Automated Diagnosis of Lung Cancer with the Use of Deep Convolutional Neural Networks on Chest CT

Cited by 3 publications

References 5 publications

Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Diplin: A Disease Risk Prediction Model Based on EfficientNetV2 and Transfer Learning Applied to Nursing Homes

Analyzing CT images for detecting lung cancer by applying the computational intelligence‐based optimization techniques

Lung Cancer Detection and Classification from Chest CT Scans Using Machine Learning Techniques

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